SAKE
SAKE is a robust method for downstream single cell RNA-seq analysis (clustering, visualization, differential expression, gene pathway enrichments). SAKE provides quantitative statistical metrics at each step of the analysis pipeline to assess the confidence in cell cluster assignments and marker identification. This tool provides several modules that include: count matrix pre-processing for quality control, cell clustering, t-SNE visualization of clusters, differential expression between clusters, and functional enrichment analysis. The aim of SAKE is to provide a user-friendly tool for easy analysis of NGS Single-Cell transcriptomic data.
If you encounter any issues or have any questions about SAKE, please check out the Github page
Download instructions
You can download the software package from GitHub, with detailed installation instructions and a full list of prerequisite libraries.
Tool Description
SAKE workflow is designed to robustly categorize gene expression profiles while avoiding unwanted noise. It utilizes a table of estimated gene abundances as input, and begins by removing samples with low total transcript counts and gene coverage. Low abundance transcripts are then filtered using median absolute deviation (MAD) to reduce stochastic dropout and technical noise. Clustering is then performed using Non-negative Matrix Factorization (NMF) with a variety of k values (corresponding to the number of clusters) and produces visual representation of the results to identify the optimal k value. After identifying the optimal cluster number, SAKE performs a large number of iterations with NMF to identify robust sample and marker membership to each cluster. The clustering results can then be visualized on t-SNE, and differential analyses and enrichment analyses can be performed to further characterize the identified clusters.
Citation
Ho Y.J., Anaparthy N., Molik D., Mathew G., Aicher T., Patel A., Hicks J. and Gale Hammell M. (2018) Single-cell RNA-seq analysis identifies markers of resistance to targeted BRAF inhibitors in melanoma cell populations. Genome Res. 28: 1353-1363. Pubmed ID: 30061114